图 书 馆
稀疏的因子载荷的贝叶斯学习Bayesian learning of sparse factor loadings |
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| 课程网址: | http://videolectures.net/bark08_rattray_blospl/ |
| 主讲教师: | Magnus Rattray |
| 开课单位: | 曼彻斯特大学 |
| 开课时间: | 2008-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Learning sparse structure is useful in many applications. For example, gene regulatory networks are sparsely connected since each gene is typically only regulated by a small number of other genes. In this case factor analysis models with sparse loading matrices have been used to uncover the regulatory network from gene expression data. In this talk I will examine the performance of sparsity priors, such as mixture and L1 priors, by calculating learning curves for Bayesian PCA in the limit of large data dimension. This allows us to address a number of questions e.g. how well can we estimate sparsity using the marginal likelihood when the prior is not well-matched to the data generating process? |
| 关 键 词: | 计算机科学; 机器学习; 贝叶斯学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-17：lxf |
| 阅读次数: | 49 |